


import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
 import onnxruntime as ort

class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
    def 						a_     (			self     ,   __snake_case=0       ):
         snake_case											=							np.random.RandomState(__snake_case       )
         snake_case											=							{
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
         }
         return inputs
    def 						a_     (			self       ):
         snake_case											=							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,   provider='''CPUExecutionProvider'''       )
         pipe.set_progress_bar_config(disable=__snake_case       )

         snake_case											=							self.get_dummy_inputs()
         snake_case											=							pipe(**__snake_case       ).images
         snake_case											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case											=							np.array([0.6_5072, 0.5_8492, 0.4_8219, 0.5_5521, 0.5_3180, 0.5_5939, 0.5_0697, 0.3_9800, 0.4_6455]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
    def 						a_     (			self       ):
         snake_case											=							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,   provider='''CPUExecutionProvider'''       )
         snake_case											=							PNDMScheduler.from_config(pipe.scheduler.config     ,   skip_prk_steps=__snake_case       )
         pipe.set_progress_bar_config(disable=__snake_case       )

         snake_case											=							self.get_dummy_inputs()
         snake_case											=							pipe(**__snake_case       ).images
         snake_case											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case											=							np.array([0.6_5863, 0.5_9425, 0.4_9326, 0.5_6313, 0.5_3875, 0.5_6627, 0.5_1065, 0.3_9777, 0.4_6330]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
    def 						a_     (			self       ):
         snake_case											=							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,   provider='''CPUExecutionProvider'''       )
         snake_case											=							LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
         pipe.set_progress_bar_config(disable=__snake_case       )

         snake_case											=							self.get_dummy_inputs()
         snake_case											=							pipe(**__snake_case       ).images
         snake_case											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case											=							np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
    def 						a_     (			self       ):
         snake_case											=							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,   provider='''CPUExecutionProvider'''       )
         snake_case											=							EulerDiscreteScheduler.from_config(pipe.scheduler.config       )
         pipe.set_progress_bar_config(disable=__snake_case       )

         snake_case											=							self.get_dummy_inputs()
         snake_case											=							pipe(**__snake_case       ).images
         snake_case											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case											=							np.array([0.5_3755, 0.6_0786, 0.4_7402, 0.4_9488, 0.5_1869, 0.4_9819, 0.4_7985, 0.3_8957, 0.4_4279]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
    def 						a_     (			self       ):
         snake_case											=							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,   provider='''CPUExecutionProvider'''       )
         snake_case											=							EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config       )
         pipe.set_progress_bar_config(disable=__snake_case       )

         snake_case											=							self.get_dummy_inputs()
         snake_case											=							pipe(**__snake_case       ).images
         snake_case											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case											=							np.array([0.5_3817, 0.6_0812, 0.4_7384, 0.4_9530, 0.5_1894, 0.4_9814, 0.4_7984, 0.3_8958, 0.4_4271]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
    def 						a_     (			self       ):
         snake_case											=							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,   provider='''CPUExecutionProvider'''       )
         snake_case											=							DPMSolverMultistepScheduler.from_config(pipe.scheduler.config       )
         pipe.set_progress_bar_config(disable=__snake_case       )

         snake_case											=							self.get_dummy_inputs()
         snake_case											=							pipe(**__snake_case       ).images
         snake_case											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 1_2_8, 1_2_8, 3)
         snake_case											=							np.array([0.5_3895, 0.6_0808, 0.4_7933, 0.4_9608, 0.5_1886, 0.4_9950, 0.4_8053, 0.3_8957, 0.4_4200]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
    def 						a_     (			self       ):
         snake_case											=							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,   provider='''CPUExecutionProvider'''       )
         pipe.set_progress_bar_config(disable=__snake_case       )

         snake_case											=							self.get_dummy_inputs()
         snake_case											=							3 * [inputs['''prompt''']]

         # forward
         snake_case											=							pipe(**__snake_case       )
         snake_case											=							output.images[0, -3:, -3:, -1]

         snake_case											=							self.get_dummy_inputs()
         snake_case											=							3 * [inputs.pop('''prompt'''       )]

         snake_case											=							pipe.tokenizer(
             __snake_case     ,   padding='''max_length'''     ,   max_length=pipe.tokenizer.model_max_length     ,   truncation=__snake_case     ,   return_tensors='''np'''     ,   )
         snake_case											=							text_inputs['''input_ids''']

         snake_case											=							pipe.text_encoder(input_ids=text_inputs.astype(np.intaa       )       )[0]

         snake_case											=							prompt_embeds

         # forward
         snake_case											=							pipe(**__snake_case       )
         snake_case											=							output.images[0, -3:, -3:, -1]

         assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()       ).max() < 1E-4




    def 						a_     (			self       ):
         snake_case											=							OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint     ,   provider='''CPUExecutionProvider'''       )
         pipe.set_progress_bar_config(disable=__snake_case       )

         snake_case											=							self.get_dummy_inputs()
         snake_case											=							3 * ['''this is a negative prompt''']
         snake_case											=							negative_prompt
         snake_case											=							3 * [inputs['''prompt''']]

         # forward
         snake_case											=							pipe(**__snake_case       )
         snake_case											=							output.images[0, -3:, -3:, -1]

         snake_case											=							self.get_dummy_inputs()
         snake_case											=							3 * [inputs.pop('''prompt'''       )]

         snake_case											=							[]
         for p in [prompt, negative_prompt]:
              snake_case											=							pipe.tokenizer(
                  __snake_case     ,   padding='''max_length'''     ,   max_length=pipe.tokenizer.model_max_length     ,   truncation=__snake_case     ,   return_tensors='''np'''     ,   )
              snake_case											=							text_inputs['''input_ids''']

              embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa       )       )[0]       )

         snake_case					,    snake_case											=							embeds

         # forward
         snake_case											=							pipe(**__snake_case       )
         snake_case											=							output.images[0, -3:, -3:, -1]

         assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()       ).max() < 1E-4






@nightly
@require_onnxruntime
@require_torch_gpu
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    @property
    def 						a_     (			self       ):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )
    @property
    def 						a_     (			self       ):
         snake_case											=							ort.SessionOptions()
         snake_case											=							False
         return options
    def 						a_     (			self       ):
         # using the PNDM scheduler by default
         snake_case											=							OnnxStableDiffusionPipeline.from_pretrained(
             '''CompVis/stable-diffusion-v1-4'''     ,   revision='''onnx'''     ,   safety_checker=__snake_case     ,   feature_extractor=__snake_case     ,   provider=self.gpu_provider     ,   sess_options=self.gpu_options     ,   )
         sd_pipe.set_progress_bar_config(disable=__snake_case       )

         snake_case											=							'''A painting of a squirrel eating a burger'''
         np.random.seed(0       )
         snake_case											=							sd_pipe([prompt]     ,   guidance_scale=6.0     ,   num_inference_steps=1_0     ,   output_type='''np'''       )
         snake_case											=							output.images

         snake_case											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         snake_case											=							np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
    def 						a_     (			self       ):
         snake_case											=							DDIMScheduler.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''     ,   subfolder='''scheduler'''     ,   revision='''onnx'''       )
         snake_case											=							OnnxStableDiffusionPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''     ,   revision='''onnx'''     ,   scheduler=__snake_case     ,   safety_checker=__snake_case     ,   feature_extractor=__snake_case     ,   provider=self.gpu_provider     ,   sess_options=self.gpu_options     ,   )
         sd_pipe.set_progress_bar_config(disable=__snake_case       )

         snake_case											=							'''open neural network exchange'''
         snake_case											=							np.random.RandomState(0       )
         snake_case											=							sd_pipe([prompt]     ,   guidance_scale=7.5     ,   num_inference_steps=1_0     ,   generator=__snake_case     ,   output_type='''np'''       )
         snake_case											=							output.images
         snake_case											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         snake_case											=							np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
    def 						a_     (			self       ):
         snake_case											=							LMSDiscreteScheduler.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''     ,   subfolder='''scheduler'''     ,   revision='''onnx'''       )
         snake_case											=							OnnxStableDiffusionPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''     ,   revision='''onnx'''     ,   scheduler=__snake_case     ,   safety_checker=__snake_case     ,   feature_extractor=__snake_case     ,   provider=self.gpu_provider     ,   sess_options=self.gpu_options     ,   )
         sd_pipe.set_progress_bar_config(disable=__snake_case       )

         snake_case											=							'''open neural network exchange'''
         snake_case											=							np.random.RandomState(0       )
         snake_case											=							sd_pipe([prompt]     ,   guidance_scale=7.5     ,   num_inference_steps=1_0     ,   generator=__snake_case     ,   output_type='''np'''       )
         snake_case											=							output.images
         snake_case											=							image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         snake_case											=							np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
    def 						a_     (			self       ):
         snake_case											=							0

         def test_callback_fn(__snake_case     ,   __snake_case     ,   __snake_case       ) -> None:
              snake_case											=							True
              nonlocal number_of_steps
              number_of_steps += 1
              if step == 0:
                   assert latents.shape == (1, 4, 6_4, 6_4)
                   snake_case											=							latents[0, -3:, -3:, -1]
                   snake_case											=							np.array(
                       [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167]       )

                   assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 1E-3
              elif step == 5:
                   assert latents.shape == (1, 4, 6_4, 6_4)
                   snake_case											=							latents[0, -3:, -3:, -1]
                   snake_case											=							np.array(
                       [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875]       )

                   assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 1E-3

         snake_case											=							False

         snake_case											=							OnnxStableDiffusionPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''     ,   revision='''onnx'''     ,   safety_checker=__snake_case     ,   feature_extractor=__snake_case     ,   provider=self.gpu_provider     ,   sess_options=self.gpu_options     ,   )
         pipe.set_progress_bar_config(disable=__snake_case       )

         snake_case											=							'''Andromeda galaxy in a bottle'''

         snake_case											=							np.random.RandomState(0       )
         pipe(
             prompt=__snake_case     ,   num_inference_steps=5     ,   guidance_scale=7.5     ,   generator=__snake_case     ,   callback=__snake_case     ,   callback_steps=1     ,   )
         assert test_callback_fn.has_been_called
         assert number_of_steps == 6




    def 						a_     (			self       ):
         snake_case											=							OnnxStableDiffusionPipeline.from_pretrained(
             '''runwayml/stable-diffusion-v1-5'''     ,   revision='''onnx'''     ,   safety_checker=__snake_case     ,   feature_extractor=__snake_case     ,   provider=self.gpu_provider     ,   sess_options=self.gpu_options     ,   )
         assert isinstance(__snake_case     ,   __snake_case       )
         assert pipe.safety_checker is None

         snake_case											=							pipe('''example prompt'''     ,   num_inference_steps=2       ).images[0]
         assert image is not None

         # check that there's no error when saving a pipeline with one of the models being None
         with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(__snake_case       )
              snake_case											=							OnnxStableDiffusionPipeline.from_pretrained(__snake_case       )

         # sanity check that the pipeline still works
         assert pipe.safety_checker is None
         snake_case											=							pipe('''example prompt'''     ,   num_inference_steps=2       ).images[0]
         assert image is not None








import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
 import jax

 from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel

class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    def __init__(			self     ,   __snake_case     ,   __snake_case=1_3     ,   __snake_case=3_0     ,   __snake_case=2     ,   __snake_case=3     ,   __snake_case=True     ,   __snake_case=True     ,   __snake_case=3_2     ,   __snake_case=5     ,   __snake_case=4     ,   __snake_case=3_7     ,   __snake_case="gelu"     ,   __snake_case=0.1     ,   __snake_case=0.1     ,   __snake_case=1_0     ,   __snake_case=0.02     ,   ):
         snake_case											=							parent
         snake_case											=							batch_size
         snake_case											=							image_size
         snake_case											=							patch_size
         snake_case											=							num_channels
         snake_case											=							is_training
         snake_case											=							use_labels
         snake_case											=							hidden_size
         snake_case											=							num_hidden_layers
         snake_case											=							num_attention_heads
         snake_case											=							intermediate_size
         snake_case											=							hidden_act
         snake_case											=							hidden_dropout_prob
         snake_case											=							attention_probs_dropout_prob
         snake_case											=							type_sequence_label_size
         snake_case											=							initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         snake_case											=							(image_size // patch_size) ** 2
         snake_case											=							num_patches + 1
    def 						a_     (			self       ):
         snake_case											=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

         snake_case											=							ViTConfig(
             image_size=self.image_size     ,   patch_size=self.patch_size     ,   num_channels=self.num_channels     ,   hidden_size=self.hidden_size     ,   num_hidden_layers=self.num_hidden_layers     ,   num_attention_heads=self.num_attention_heads     ,   intermediate_size=self.intermediate_size     ,   hidden_act=self.hidden_act     ,   hidden_dropout_prob=self.hidden_dropout_prob     ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,   is_decoder=__snake_case     ,   initializer_range=self.initializer_range     ,   )

         return config, pixel_values
    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							FlaxViTModel(config=__snake_case       )
         snake_case											=							model(__snake_case       )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         snake_case											=							(self.image_size, self.image_size)
         snake_case											=							(self.patch_size, self.patch_size)
         snake_case											=							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape     ,   (self.batch_size, num_patches + 1, self.hidden_size)       )
    def 						a_     (			self     ,   __snake_case     ,   __snake_case       ):
         snake_case											=							self.type_sequence_label_size
         snake_case											=							FlaxViTForImageClassification(config=__snake_case       )
         snake_case											=							model(__snake_case       )
         self.parent.assertEqual(result.logits.shape     ,   (self.batch_size, self.type_sequence_label_size)       )

         # test greyscale images
         snake_case											=							1
         snake_case											=							FlaxViTForImageClassification(__snake_case       )

         snake_case											=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
         snake_case											=							model(__snake_case       )




    def 						a_     (			self       ):
         snake_case											=							self.prepare_config_and_inputs()
         (
             (
             snake_case
         )					,    (
             snake_case
         )					,    
         )											=							config_and_inputs
         snake_case											=							{'''pixel_values''': pixel_values}
         return config, inputs_dict






@require_flax
class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
    def 						a_     (			self       ):
         snake_case											=							FlaxViTModelTester(self       )
         snake_case											=							ConfigTester(self     ,   config_class=__snake_case     ,   has_text_modality=__snake_case     ,   hidden_size=3_7       )
    def 						a_     (			self       ):
         self.config_tester.run_common_tests()
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__snake_case       )
    def 						a_     (			self       ):
         snake_case											=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*__snake_case       )
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              snake_case											=							model_class(__snake_case       )
              snake_case											=							inspect.signature(model.__call__       )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              snake_case											=							[*signature.parameters.keys()]

              snake_case											=							['''pixel_values''']
              self.assertListEqual(arg_names[:1]     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case					,    snake_case											=							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__       ):
                   snake_case											=							self._prepare_for_class(__snake_case     ,   __snake_case       )
                   snake_case											=							model_class(__snake_case       )

                   @jax.jit
                   def model_jitted(__snake_case     ,   **__snake_case       ):
                        return model(pixel_values=__snake_case     ,   **__snake_case       )

                   with self.subTest('''JIT Enabled'''       ):
                        snake_case											=							model_jitted(**__snake_case       ).to_tuple()

                   with self.subTest('''JIT Disabled'''       ):
                        with jax.disable_jit():
                             snake_case											=							model_jitted(**__snake_case       ).to_tuple()

                   self.assertEqual(len(__snake_case       )     ,   len(__snake_case       )       )
                   for jitted_output, output in zip(__snake_case     ,   __snake_case       ):
                        self.assertEqual(jitted_output.shape     ,   output.shape       )




    @slow
    def 						a_     (			self       ):
         for model_class_name in self.all_model_classes:
              snake_case											=							model_class_name.from_pretrained('''google/vit-base-patch16-224'''       )
              snake_case											=							model(np.ones((1, 3, 2_2_4, 2_2_4)       )       )
              self.assertIsNotNone(__snake_case       )




